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Spatial variability of long-term trends in significant wave height over the Gulf of Gdańsk using System Identification techniques

Jordan Badur
Jordan Badur
 et 
Witold Cieślikiewicz
Witold Cieślikiewicz
   | 18 juin 2018
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Article Category: Original research paper
Publié en ligne: 18 juin 2018
Pages: 190 - 201
Reçu: 29 août 2017
Accepté: 21 nov. 2017
DOI: https://doi.org/10.1515/ohs-2018-0018
© 2018 Faculty of Oceanography and Geography, University of Gdańsk, Poland
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1

Flow diagram for wind velocity and significant wave height simulations. Non-simulated data, used for model identification and simulation, are included in solid-line rectangles. The simulated data are denoted by dotted-line rectangles; rectangles with rounded corners represent models. Dataset descriptions are provided in Table 1.
Flow diagram for wind velocity and significant wave height simulations. Non-simulated data, used for model identification and simulation, are included in solid-line rectangles. The simulated data are denoted by dotted-line rectangles; rectangles with rounded corners represent models. Dataset descriptions are provided in Table 1.

Figure 2

Data grids: NOAA 20CRv2 (black squares), UMPL-ICM (blue dots) and 1 NM nested WAM grid (small green dots)
Data grids: NOAA 20CRv2 (black squares), UMPL-ICM (blue dots) and 1 NM nested WAM grid (small green dots)

Figure 3

Root Mean Square Error (left) and scatter index (right) values for significant wave height simulation using the Takagi-Sugeno-Kang simulation method with m-ARX downscaled winds
Root Mean Square Error (left) and scatter index (right) values for significant wave height simulation using the Takagi-Sugeno-Kang simulation method with m-ARX downscaled winds

Figure 4

Significant wave height: Takagi-Sugeno-Kang simulated using downscaled winds (right) and WAM modeled with UMPL-ICM winds (left). Peaks of validation storms no. 16–18
Significant wave height: Takagi-Sugeno-Kang simulated using downscaled winds (right) and WAM modeled with UMPL-ICM winds (left). Peaks of validation storms no. 16–18

Figure 5

NH cumulative change due to the linear trend over the simulation period expressed as a percentage of NH mean
NH cumulative change due to the linear trend over the simulation period expressed as a percentage of NH mean

Datasets used for identification, validation and simulation of local wind velocity and significant wave height Hs

Data set Used… Description
UMPL-ICM winds… to run the WAM model. As an input for the identification of wind scaling models. Half of data used for the verification. 9 NM, 3 h, interpolated into 1 NM, 1 h grid, 1998–2001, 28 storms
20CRv2 winds… as an input for the identification and validation of wind scaling models. 120 NM, 3 h, means interpolated into 1NM 1 h grid, 1871–2008
Simulated winds… for Hs simulation. Generated using scaling models with 20CRv2 data as an input. 1 NM, 1 h, 1871–2008
WAM Hs… to identify Hs simulation models. Half of data used for verification. Generated using a locally nested WAM model and UMPL-ICM winds. 1 NM, 3 h, 1998–2001, 28 storms
Simulated Hs… to generate the number of hours with Hs exceeding a given threshold. Simulated using previously identified Hs models and simulated winds. 1 NM, 1 h, 1871–2008
eISSN:
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